{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 讀取房屋資料"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>title</th>\n",
       "      <th>age</th>\n",
       "      <th>area</th>\n",
       "      <th>floor_info</th>\n",
       "      <th>direction</th>\n",
       "      <th>layout</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://esf.sh.fang.com/chushou/3_289079207.htm</td>\n",
       "      <td>新出笋盘,业主急售,低于市场价30万,精装南北通,近11/12号线</td>\n",
       "      <td>建筑年代：1998\\r\\n</td>\n",
       "      <td>65</td>\n",
       "      <td>中层(共6层)\\r\\n</td>\n",
       "      <td>南北向\\r\\n</td>\n",
       "      <td>\\r\\n                            2室1厅\\r\\n      ...</td>\n",
       "      <td>3500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://esf.sh.fang.com/chushou/3_290295785.htm</td>\n",
       "      <td>业主急抛,低于市场价50万,双南双天井,闹中取静,近3/12号线!</td>\n",
       "      <td>建筑年代：1996\\r\\n</td>\n",
       "      <td>90</td>\n",
       "      <td>低层(共6层)\\r\\n</td>\n",
       "      <td>南北向\\r\\n</td>\n",
       "      <td>\\r\\n                            3室2厅\\r\\n      ...</td>\n",
       "      <td>5000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://esf.sh.fang.com/chushou/3_290971789.htm</td>\n",
       "      <td>新出笋盘 近11号线 东边套 低于市价50万 送20平米花园 急售</td>\n",
       "      <td>建筑年代：1998\\r\\n</td>\n",
       "      <td>72</td>\n",
       "      <td>低层(共6层)\\r\\n</td>\n",
       "      <td>南向\\r\\n</td>\n",
       "      <td>\\r\\n                            2室1厅\\r\\n      ...</td>\n",
       "      <td>3980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>http://esf.sh.fang.com/chushou/3_290987582.htm</td>\n",
       "      <td>业主出国急售:板式两房,精装修全明,带产权车位,看房方便!</td>\n",
       "      <td>建筑年代：2006\\r\\n</td>\n",
       "      <td>96</td>\n",
       "      <td>高层(共7层)\\r\\n</td>\n",
       "      <td>南北向\\r\\n</td>\n",
       "      <td>\\r\\n                            2室2厅\\r\\n      ...</td>\n",
       "      <td>5500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>http://esf.sh.fang.com/chushou/3_291070232.htm</td>\n",
       "      <td>徐汇滨江板块,双南两居,小高层带电梯,使用面积大,近3,11号线</td>\n",
       "      <td>建筑年代：1996\\r\\n</td>\n",
       "      <td>86</td>\n",
       "      <td>中层(共7层)\\r\\n</td>\n",
       "      <td>南向\\r\\n</td>\n",
       "      <td>\\r\\n                            2室1厅\\r\\n      ...</td>\n",
       "      <td>4500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              url  \\\n",
       "0  http://esf.sh.fang.com/chushou/3_289079207.htm   \n",
       "1  http://esf.sh.fang.com/chushou/3_290295785.htm   \n",
       "2  http://esf.sh.fang.com/chushou/3_290971789.htm   \n",
       "3  http://esf.sh.fang.com/chushou/3_290987582.htm   \n",
       "4  http://esf.sh.fang.com/chushou/3_291070232.htm   \n",
       "\n",
       "                               title                                    age  \\\n",
       "0  新出笋盘,业主急售,低于市场价30万,精装南北通,近11/12号线  建筑年代：1998\\r\\n                           \n",
       "1  业主急抛,低于市场价50万,双南双天井,闹中取静,近3/12号线!  建筑年代：1996\\r\\n                           \n",
       "2  新出笋盘 近11号线 东边套 低于市价50万 送20平米花园 急售  建筑年代：1998\\r\\n                           \n",
       "3      业主出国急售:板式两房,精装修全明,带产权车位,看房方便!  建筑年代：2006\\r\\n                           \n",
       "4   徐汇滨江板块,双南两居,小高层带电梯,使用面积大,近3,11号线  建筑年代：1996\\r\\n                           \n",
       "\n",
       "   area                               floor_info  \\\n",
       "0    65  中层(共6层)\\r\\n                               \n",
       "1    90  低层(共6层)\\r\\n                               \n",
       "2    72  低层(共6层)\\r\\n                               \n",
       "3    96  高层(共7层)\\r\\n                               \n",
       "4    86  中层(共7层)\\r\\n                               \n",
       "\n",
       "                             direction  \\\n",
       "0  南北向\\r\\n                               \n",
       "1  南北向\\r\\n                               \n",
       "2   南向\\r\\n                               \n",
       "3  南北向\\r\\n                               \n",
       "4   南向\\r\\n                               \n",
       "\n",
       "                                              layout    price  \n",
       "0  \\r\\n                            2室1厅\\r\\n      ...  3500000  \n",
       "1  \\r\\n                            3室2厅\\r\\n      ...  5000000  \n",
       "2  \\r\\n                            2室1厅\\r\\n      ...  3980000  \n",
       "3  \\r\\n                            2室2厅\\r\\n      ...  5500000  \n",
       "4  \\r\\n                            2室1厅\\r\\n      ...  4500000  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "df = pandas.read_excel('Data\\house_price_regression.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 房屋資料預處理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['age'] = df['age'].map(lambda e: 2017 - int(e.strip().strip('建筑年代：')) )\n",
    "df[['room', 'living_room']] = df['layout'].str.extract('(\\d+)室(\\d+)厅')\n",
    "df['room'] = df['room'].astype(int)\n",
    "df['living_room'] = df['living_room'].astype(int)\n",
    "df['total_floor'] = df['floor_info'].str.extract('共(\\d+)层')\n",
    "df['total_floor'] = df['total_floor'].astype(int)\n",
    "df['floor']       = df['floor_info'].str.extract('^(.)层')\n",
    "df['direction']   = df['direction'].map(lambda e: e.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>title</th>\n",
       "      <th>age</th>\n",
       "      <th>area</th>\n",
       "      <th>floor_info</th>\n",
       "      <th>direction</th>\n",
       "      <th>layout</th>\n",
       "      <th>price</th>\n",
       "      <th>room</th>\n",
       "      <th>living_room</th>\n",
       "      <th>total_floor</th>\n",
       "      <th>floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://esf.sh.fang.com/chushou/3_289079207.htm</td>\n",
       "      <td>新出笋盘,业主急售,低于市场价30万,精装南北通,近11/12号线</td>\n",
       "      <td>19</td>\n",
       "      <td>65</td>\n",
       "      <td>中层(共6层)\\r\\n</td>\n",
       "      <td>南北向</td>\n",
       "      <td>\\r\\n                            2室1厅\\r\\n      ...</td>\n",
       "      <td>3500000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>中</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://esf.sh.fang.com/chushou/3_290295785.htm</td>\n",
       "      <td>业主急抛,低于市场价50万,双南双天井,闹中取静,近3/12号线!</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>低层(共6层)\\r\\n</td>\n",
       "      <td>南北向</td>\n",
       "      <td>\\r\\n                            3室2厅\\r\\n      ...</td>\n",
       "      <td>5000000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>低</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://esf.sh.fang.com/chushou/3_290971789.htm</td>\n",
       "      <td>新出笋盘 近11号线 东边套 低于市价50万 送20平米花园 急售</td>\n",
       "      <td>19</td>\n",
       "      <td>72</td>\n",
       "      <td>低层(共6层)\\r\\n</td>\n",
       "      <td>南向</td>\n",
       "      <td>\\r\\n                            2室1厅\\r\\n      ...</td>\n",
       "      <td>3980000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>低</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>http://esf.sh.fang.com/chushou/3_290987582.htm</td>\n",
       "      <td>业主出国急售:板式两房,精装修全明,带产权车位,看房方便!</td>\n",
       "      <td>11</td>\n",
       "      <td>96</td>\n",
       "      <td>高层(共7层)\\r\\n</td>\n",
       "      <td>南北向</td>\n",
       "      <td>\\r\\n                            2室2厅\\r\\n      ...</td>\n",
       "      <td>5500000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>http://esf.sh.fang.com/chushou/3_291070232.htm</td>\n",
       "      <td>徐汇滨江板块,双南两居,小高层带电梯,使用面积大,近3,11号线</td>\n",
       "      <td>21</td>\n",
       "      <td>86</td>\n",
       "      <td>中层(共7层)\\r\\n</td>\n",
       "      <td>南向</td>\n",
       "      <td>\\r\\n                            2室1厅\\r\\n      ...</td>\n",
       "      <td>4500000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>中</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              url  \\\n",
       "0  http://esf.sh.fang.com/chushou/3_289079207.htm   \n",
       "1  http://esf.sh.fang.com/chushou/3_290295785.htm   \n",
       "2  http://esf.sh.fang.com/chushou/3_290971789.htm   \n",
       "3  http://esf.sh.fang.com/chushou/3_290987582.htm   \n",
       "4  http://esf.sh.fang.com/chushou/3_291070232.htm   \n",
       "\n",
       "                               title  age  area  \\\n",
       "0  新出笋盘,业主急售,低于市场价30万,精装南北通,近11/12号线   19    65   \n",
       "1  业主急抛,低于市场价50万,双南双天井,闹中取静,近3/12号线!   21    90   \n",
       "2  新出笋盘 近11号线 东边套 低于市价50万 送20平米花园 急售   19    72   \n",
       "3      业主出国急售:板式两房,精装修全明,带产权车位,看房方便!   11    96   \n",
       "4   徐汇滨江板块,双南两居,小高层带电梯,使用面积大,近3,11号线   21    86   \n",
       "\n",
       "                                floor_info direction  \\\n",
       "0  中层(共6层)\\r\\n                                   南北向   \n",
       "1  低层(共6层)\\r\\n                                   南北向   \n",
       "2  低层(共6层)\\r\\n                                    南向   \n",
       "3  高层(共7层)\\r\\n                                   南北向   \n",
       "4  中层(共7层)\\r\\n                                    南向   \n",
       "\n",
       "                                              layout    price  room  \\\n",
       "0  \\r\\n                            2室1厅\\r\\n      ...  3500000     2   \n",
       "1  \\r\\n                            3室2厅\\r\\n      ...  5000000     3   \n",
       "2  \\r\\n                            2室1厅\\r\\n      ...  3980000     2   \n",
       "3  \\r\\n                            2室2厅\\r\\n      ...  5500000     2   \n",
       "4  \\r\\n                            2室1厅\\r\\n      ...  4500000     2   \n",
       "\n",
       "   living_room  total_floor floor  \n",
       "0            1            6     中  \n",
       "1            2            6     低  \n",
       "2            1            6     低  \n",
       "3            2            7     高  \n",
       "4            1            7     中  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del df['layout']\n",
    "del df['floor_info']\n",
    "del df['title']\n",
    "del df['url']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>area</th>\n",
       "      <th>direction</th>\n",
       "      <th>price</th>\n",
       "      <th>room</th>\n",
       "      <th>living_room</th>\n",
       "      <th>total_floor</th>\n",
       "      <th>floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>65</td>\n",
       "      <td>南北向</td>\n",
       "      <td>3500000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>中</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>南北向</td>\n",
       "      <td>5000000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>低</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>72</td>\n",
       "      <td>南向</td>\n",
       "      <td>3980000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>低</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>96</td>\n",
       "      <td>南北向</td>\n",
       "      <td>5500000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>高</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>86</td>\n",
       "      <td>南向</td>\n",
       "      <td>4500000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>中</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  area direction    price  room  living_room  total_floor floor\n",
       "0   19    65       南北向  3500000     2            1            6     中\n",
       "1   21    90       南北向  5000000     3            2            6     低\n",
       "2   19    72        南向  3980000     2            1            6     低\n",
       "3   11    96       南北向  5500000     2            2            7     高\n",
       "4   21    86        南向  4500000     2            1            7     中"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pandas.concat([df, pandas.get_dummies(df['direction']), pandas.get_dummies(df['floor'])], axis = 1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>area</th>\n",
       "      <th>direction</th>\n",
       "      <th>price</th>\n",
       "      <th>room</th>\n",
       "      <th>living_room</th>\n",
       "      <th>total_floor</th>\n",
       "      <th>floor</th>\n",
       "      <th>东南向</th>\n",
       "      <th>东向</th>\n",
       "      <th>南北向</th>\n",
       "      <th>南向</th>\n",
       "      <th>西南向</th>\n",
       "      <th>西向</th>\n",
       "      <th>中</th>\n",
       "      <th>低</th>\n",
       "      <th>高</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>65</td>\n",
       "      <td>南北向</td>\n",
       "      <td>3500000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>中</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>南北向</td>\n",
       "      <td>5000000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>低</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>72</td>\n",
       "      <td>南向</td>\n",
       "      <td>3980000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>低</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>96</td>\n",
       "      <td>南北向</td>\n",
       "      <td>5500000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>高</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>86</td>\n",
       "      <td>南向</td>\n",
       "      <td>4500000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>中</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  area direction    price  room  living_room  total_floor floor  东南向  \\\n",
       "0   19    65       南北向  3500000     2            1            6     中    0   \n",
       "1   21    90       南北向  5000000     3            2            6     低    0   \n",
       "2   19    72        南向  3980000     2            1            6     低    0   \n",
       "3   11    96       南北向  5500000     2            2            7     高    0   \n",
       "4   21    86        南向  4500000     2            1            7     中    0   \n",
       "\n",
       "   东向  南北向  南向  西南向  西向  中  低  高  \n",
       "0   0    1   0    0   0  1  0  0  \n",
       "1   0    1   0    0   0  0  1  0  \n",
       "2   0    0   1    0   0  0  1  0  \n",
       "3   0    1   0    0   0  0  0  1  \n",
       "4   0    0   1    0   0  1  0  0  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del df['南北向']\n",
    "del df['低']\n",
    "del df['direction']\n",
    "del df['floor']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>area</th>\n",
       "      <th>price</th>\n",
       "      <th>room</th>\n",
       "      <th>living_room</th>\n",
       "      <th>total_floor</th>\n",
       "      <th>东南向</th>\n",
       "      <th>东向</th>\n",
       "      <th>南向</th>\n",
       "      <th>西南向</th>\n",
       "      <th>西向</th>\n",
       "      <th>中</th>\n",
       "      <th>高</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>65</td>\n",
       "      <td>3500000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>5000000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>72</td>\n",
       "      <td>3980000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>96</td>\n",
       "      <td>5500000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>86</td>\n",
       "      <td>4500000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  area    price  room  living_room  total_floor  东南向  东向  南向  西南向  西向  \\\n",
       "0   19    65  3500000     2            1            6    0   0   0    0   0   \n",
       "1   21    90  5000000     3            2            6    0   0   0    0   0   \n",
       "2   19    72  3980000     2            1            6    0   0   1    0   0   \n",
       "3   11    96  5500000     2            2            7    0   0   0    0   0   \n",
       "4   21    86  4500000     2            1            7    0   0   1    0   0   \n",
       "\n",
       "   中  高  \n",
       "0  1  0  \n",
       "1  0  0  \n",
       "2  0  0  \n",
       "3  0  1  \n",
       "4  1  0  "
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 繪製散佈圖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc651ba8>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7+iICbepzu6t6r/xM1ASckM5BCQGE+Cll0+m9pRex/vPTpppk9zRWNqgvSY7R\nzXYpnXeok8GNamxlCzIes4truMubyj1epTX1eGNfmdsm6j+YFItthRe441YoEx+0S7lLJw/HxHsH\nYsX2YgQwhpt31PfGqOq99jOtyznr2rdGTcAJ8R2aOSOkCylnO5Q8bZLdE/HS+3lCAxnCggOFgRmg\nbmxuVGMLAKL72rnP88ZFTdT/JbPAsLo6r4uB1QzVfSW1woxUoK122TMfFaGx2akKzMKCAwyr3ov6\nc1ITcEJ8j4IzQrrIvI0H8NSWY9haUI2nthzD/I0HXM950iS7J3tp8VjszZiOX85KQB+7/sfWj747\nHKVrHhHOdinJS5/uamzVNjRzn+eNi+qK7TpZ57Y/4QfLUjElfgACbcCU+AHcTE0j7uq1AQDT/KgP\nCwrAvz36AA6tmCWc/TJTK027HOqPbZgI6Y4oOCOkC/BmO+Sq9YD1Jtm9QUJUOP5pSpyuoVGI3YZf\nzx0JAFhsoqfkxNj+AMS1tORx0bl44+56WRr1J4x7bhfyK66hxQnkV1zjlpYwYqZem6S5a62ShJn3\nDzFMAnD3mbTLoXHP7ULmV9Vw3GxG5lfVlj8HIeQuCs4I6QKi2Q553ErT6d7EXTPq9Cnx6B8SYHiO\ngX1DAEBYskEe552rf0gAd78Zr4m6Fi+x4ReZR7nHyuOZ+RV4/J18wySE2UnRGBUVJnx+6eTh2PD4\neOE9Ey2tiz4T7xzulogJIdZQKQ1CuoDZMg9WC4/2Fo6GJsNm1Jn5Fcj88huc5uzF0tYQc1djy0qd\ns025ZfjLsWpUXNZ3odubMV1XR+yBFz/DzTv6HpphQTYE2pilMh77Smqxu6QO85KicG9kmK6OHe+e\naTso8Irz8mqlae/7xN/vhuOmfrk3MsyOY7+bJ7xmQnoDT0ppULYmIV1Anu04o/nFqC3zYKXpdG/i\nrhl1+pR4pE+Jx482H3HbDmrN98dhzffF73WpvhGX6ptwqb7R7XXJTdR5XRh4BV5njRyEHScv6sbj\nB4biZK06sJTLeIgCxNlJ0arvj/b9tPfM3dK6HOgpG8Mruzcoz9XRNkwbc0qRXVyLxcnRyJg/2tRr\nCOnJaOaMkC6knO0wWxiVmGe2NZJI4spdaFb8iLQzoGydudZCZrsw8PZmTYqLwNHKq9zxj34+xdT7\nuyPqoNAv2IYbTXdn8+TZNHf30tM2TB25x4R0B1SElpBuZnZSNNanjaPAzAcKKhyqYAIADpY7UFDh\nELxCbWOUKf8uAAAgAElEQVROqSpoAIBmqW3cjISocKSlxLptiVT5ygI8OmYIwoJseHTMEFS+sgBz\nRg3mHisa94QokUAZmAFts2mbcsvc3svKVxYg/TvDEBlmR/p3hpkKzDp6jwnpqSg4I4T0SHllly2N\na2UX88uWiMY74u30STj10sN4O30SACA1gR+EicY9wUsk6CdIphCV1dDeyzXfH4djv5tnuj9mZ95j\nQroTCs4IIT3S9MRBlsa1FifzZzNF4940LCIUIZp6biF2m7CSv5bZNks5GTPw/tKJeDJlGN5fOhEb\nnxjPPU5UVsPsvRTpyntMiD+jhABCiFfx9lq5yzpdu+Mkdp6sxcIx0Xjh0TEeva9ys/r1283YXVKH\npJi+KKlpcB0zPKKtjIYyQzM2oo8rG1He+A4AGfNH4w8HzqmW3QIZMCspBo6GJp82CpdLhjy7vRh2\nm83VLsnMe2YXXcCz244jgNnQKjnxWto4wzZL2kQCXqLK8pmJ+OLvDrfJFUruMmoB/j22M1BSAOn1\nKCGAEOI1L2adwJYj6izF3adqVa2oYvoFqXqEjnhul6pEqg3AORP7lZS0m9U9FRrIdP0w5UzCkUP6\nIq/8sipY8nVvSTMBjvb47768Fy2KGxpoA758fo6lYJKXqGJlw3920QWs0ASWRveKsjVJT0YJAYSQ\nLlNeV68KzIC2yvhGPULX7jipq/jvbB83i7fx31PK3puyjPmjsf3/n4K88stobHZ2am/JyL7BGBc7\nwHRgderbG6rADABanG3j7pTX1WNbQRXK6+p1iSo/3nyY+xreuKOhCSu2F1u6VxnzR+PAMzMpMCOk\nHQVnhBCv4FXAF5F7hO48KeghKhjnMbvB3yze5vfqq7dht6l/XPJ6S3Y90UqI8QrJi1knMGdjHp7e\nVsztBZpfcYX7Ot5497lXhPgvCs4I6UV82Zh6fOwA08fKPUIXjhH0EBWM83R0U7oWb/P7sIhQNDvV\nU1La3pL+4IF7+sMeoG65ZA9geOCe/sLXiGY8lb1Ap8QP5L6WN95d7hUh/oyCM0K8QLkk5K983Zg6\nISocSycPV40tnTzcsEfoC4+O0f0QsrWPG1H2g0yJj8S0BPHGdCtCA5kqKUDmrqcnj5m+mN4W2TcY\n//74OAQH2tAnKADBgTb8++PjDK/zD7nl3HHlTOgfl03mHsMb9+ReEULUKCGAkA7ibYJ/afHYLrwi\nvVUfH+e210n/zjDTNanM8nW2pqgfpDZb85d/+ZrbtzIqPBhzRw92m63JY3aD/rjVn1vqi+ltnl6n\nkrYXqNXvudVkBkJ6Kk8SAig4I6QDyuvqMWdjnm6c1+S6K/WUxtRmG8YDbRmAb+ae0x37q5kjfLrx\nPDO/Aqs+KdGNr1mU5LZxemcSXSegD7y6y/ecEH9E2ZqEdDLRJngrm+M7g6gBtdnG1J4oqHDg9d1n\nTLdLMmN3SZ3p8Yz5o2FXb7/qlBpa2YJq+qLxriK6njH3hOtmxPzpe74xpxQzXsulFk+kR6PgjJAO\nEG2Ct7I5vjOIli69vaQpS998BGmbjuCt/eVI23QEP9p8xCvnFfWDFI2XrVuAX80cgbjIPvjVzBGd\n0lB7saCavmi8q4iu5wcp+mVnf/meJ67chTdzz6HScQtv5p5D4krv7pskxF9QcEZIB4g2wctLPWbb\n6HQGTxpTe6KjDceVtIkWvH6Qo6LCDBvHd3YNrfQp8eiv6VHZL9iGsbERfvE9kPGus39IAHfp1d33\nvDNQk3TSm9CeM0K8gLcJ3mqV9J7i9d1n8NZ+fQbgL2cl4DfzRpk+j9EGdF4Fe3+TmV+B7OIa3Duw\nD3aeqPHb74F8nYuTY9zuieN9zzvLjNdyUem4pRuPi+yDA8/M7NRrIcQKSgggxE84Gpowdf1+NDbf\nzRYMsdtwaMWsHp+5VlDhQNom/TLmtuWphr0YlXrKBvTe/D3wtq5K8CCkozwJzqjxOSE+IFdJb1Q0\nJ5KrpPvzL2VRyQt5xiTQBpy7fAvTEwfhdM0N7oyLXHfMSpNsLaMN6AlR4diUW2ZY/sJXZRyUM3ZH\n/n7ZbfkP3vegsdmJqa/sQ9zAULCAAFMlPDqbP5bBoCbppDehmTNCfKA7zpikvryH26Bcu7zIw6vj\npaw7ZiUwA4xnzh59+yBut9z9uaVtVu6r5WRtfTUlUbN23veAh9dwvav4+3I8NUkn3Q2V0iDET/hT\nlXQz3QuyCqu4Dco35Za5DcwA4Hpjq64Sfkp8JH4zb5TlwAwQb0DfV1KrCswAdbNyT5pum7GvpFYY\nmAHiZu3K74ERXsP1ruCr++dN1CSd9Aa0rEmIjywaPxRTEwZ16fKQ2aruciNyLV4TcJHs4hqvFll9\nafFYLE2NU21Af/hN/Wwa0Hady2cm+mw5WVRfTWnnyVru8qb8PZj6yj40tohXKuTP0JW663I8IT0N\nzZwR4kORfYMxLnZAl82YuWtoLZMbkWvxmoCL+KKOV0JUONJSYl1JAKLrkcd91XRbVEdNyahZe2Tf\nYMy9f7Dh663ca1+hpuWE+AcKzgjpoaxUdV8yIZbboHz5zETd8iIPrz6Wssbb2h0nMXndXu7SnxXL\nZyYiNFBd9l/ZrNxXy8m8+mpKZpq1v50+SficqOF6R+vkmVnSVvKn5XhCejNKCCCkh/KkHIU3sjUB\n9aby+qYW1XOizfNWdJdsTZ5fZB7F/rOXETsgxDBbs6Mb8602Klfyx2xNQrorqnNGCFF5MfsEthz2\n7Be0p8xkKP506r2WAprepqPZvj2lThwhPQHVOSOEqPA21fsab1O5VlZRjVeCs66sWO9LHd2Y765O\nHCHEv1FwRkgPlxAV3qm/kHmbyrXuj+nb4ffpyLKdv+voxnx/aVROCPEMJQQQQtzKzK/A4+/k62qZ\n8cibyo1+uPxrB2fNrGSidkcd3ZjvD43KCSGeo5kzQoihcas/x/XGVgDA0cqreG33GV03AK3fZZ0Q\nLmp6I0joDct2Ha2T1xVL2oQQ7+iUmTPG2BOMsQrGWDlj7Cec51czxqoYY5WMsSmdcU2EEPcy8ytc\ngZmM1w3A3WsA4B/Gx2BvxnSvLD3667LdvpJarNh2HPtK+EV9reponTxtnThCSPfg85kzxlg4gH8H\nkAqgFUARY2yHJEmX2p//CYAUACMBNAKgvG1C/ES2oENAdnENYgaEuspKzE6Kdvua6muNpoKEx/9w\nEIVVNzAhth8++udp3GPkZTtlJurU+wbilc9PY+HYaHxRdgl7Si9i7ugh2PDkBLfv6Q3K3ptbC6ox\nKioMORkzAHRdP0h3JUe6u57++Ujv5fNSGoyxNABLJElKb3/8fwF8IknSX9ofnwDwD5IklZs5H5XS\nIKTzZOZXYNUnJbrxIX3tuNjQ7HqsDEREr1mzKMlte6e453bpxioFNdFGPLfLIB/U3Dm8ZV9JLZ7a\nckw3/v7Sifj5B8fQrPgxa2dA2TrfXg8AjF71qWGD+O6up38+0nP4a+PzWADfKB5XA4gBAMaYHUA0\ngKcYY2cYY39ljFnvkkwI8Yn0KfHoHxKgGgsNhCowA4AzdTddS3miACy7uMZwOfTxPxw0Pb52x0nT\ngRkAPL210MLR1ol6b27IOa0KzACgWWqbSfOlTbllhg3iu7ue/vkI6YzgLAhQ/Rx1om15EwAGAYgA\nsB/A/QDOA3hBewLG2M8YYwWMsYJLly75+HIJIUrHVz+ENYuSMCkuAmsWJWHR+GHc4+QARRR4HK28\nilWflGDc6s+5zxdW3TA9vvOktT1de0ovWjreKlHvTcfNZu54drF39qSJiBrWW2lk7896+ucjpDOC\nsxoAyp4jwwBUtf/9MoAGSZL2SG3rq9kARmlPIEnSu5IkpUiSlDJ4sHHzYEKI96VPicdHP5+C9Cnx\nwkBEHncXeIgSCibE9uMezxs3ajLOM3f0EEvHW8XrvTkqKgz/OIkfyC5Otnb9VrlrEN/d9fTPR0hn\nBGe7AcxnjA1hjEUDmNI+BkmSmgF8yRiT8/IXAjjaCddECPGQKBCRkwLMBB68pAHR5n/e+AuPjrH0\nw6szkgJyMmbg/aUT8WTKMLy/dCJyMmYgY/5o2NV92mFn8HlSgLsG8d1dT/98hHRKb03G2I8B/K79\n4dPt/3ufJEkbGGMjAHwAIAptgdkySZJuis5FCQGE+AdlE3BltiYAJK7cpdtrpWSUHGAmW1O2dsdJ\nV/PxB4b2dzVt74psTSOUrekbPf3zkZ6BGp8TQvzGxpxS/Hd+Jeqb9Fv3fzVzhDBIEQUyjoYm0wVZ\nrRzrb4yCXkJI90PBGSHEr8x4LReVjlu68bjIPjjwzEzduHbGTS47kV10ASu2F8Nus6HZ6cSrjyVj\n0fihutcDsHSsv1HWSgPUJUoIId2Tv5bSIIT0UqL9Z7zxjTml3LITa3ecxIrtxWhsdqK+qQWNzU48\nu70YjoYm3TkcDU2mj/U3+0pqVYEZoC5RQgjpPSg4I4S4OBqacLzqmi6YySqswrI/HUVWYZXglXxW\nNsSLsjz/O/8btDarl0btNhuqr97WXXvu6YsIYOo3tAH4U36l3zRFL6hw4PXdZ1BQ4VCNi2qlicYJ\nIT0XLWsS0k1k5lcgu7gGi5Nj3Fba94R2OfBH370XNxqbkXPqW1y7fTc4iukXhMPPz7V0bjMb4jfm\nlOLN3HOmzhdit+HQilmu/WTytQfaGBqa9H09ZUsnD/dKb09PpW8+gi/K7wZl0xIi8cGyVADGXQZo\n7xkh3RftOSOkhxq3+nNVM/H+IQE4vrqtAo03MtYcDU2Yun4/GpvN1d1/44lkNDS2dDhY1G7cd5fl\naQcQYLfhN3NGYmBYEMbHDkBEWBD32oMDAF6ctjdjurDHpxxEjhzcB05mw8Kx0VgyIdajz6ZVUOFA\n2qYjuvFty1MRP7gvqq/exm8//Brll+7u0evonjNPEyOUrys6f5USFAjpAE+CM583PieEdExmfoUq\nMAPuFnJd+2mpq41NaU093thX5lF/weqrt2G32dBosinSbz4sdh15tPIqXtt9xhUsmsXbuF+2bgE2\n5pTiP/92Di2cSxka2QffiYvAy5+ddo09MjaKe+281wNAUdU1bnCmDAzlJIa9pRex/vPTlmcKefLK\nLnPH3ztYgb+VXYLdZkN9U0uH30fmaWKE8nUNTS2QY2VtM3dCiO/QnjNC/ByvYCsA/Nff/u61/oLD\nIkLR7DTfrVJ7pKjqv4jRxv2M+aPxzNyR3NfNvX8wPjx2QTX26Yk63GnVX3urYAZufOwA3RgvGUFW\nc+OO5b12PNMTB3HH95+uc90HLU8TAjxNjNC+TntLKEGBkM5hKThjjA1ijFmamiOEdMxiQUuaFsGW\nBE/6C0b2DcarjyUjxG6Dps+5TqDgp4YoiOSpvnobzZqpreYWp2uTv6gC/KiY/tzzPTZhKELsNvQJ\nMr74KSMGcsfdtZzaeaLjAUlKfCSmJUSqxsYP648Qu/EChicJAfJMqBIvicLM67xxPYQQa0wHZ4yx\nnwH4AsBf2x9PZ4y97qsLI4S0SZ8Sj/6aiKl/SAB+MjmOe7yn/QUXjR+KQytmYevPpyJhcB/hcfcO\n5D8nCiJ5mltadTNbrVLbuKx0zSNYOX8kRseEY+X8kShd8wh31gsAfjI1HodWzMI76RMQrAnqlPLP\nXcGcjXl4MfuE5tqN91ItHOudvVYfLEvFtuWp+OWsBGxbnor3fzzJ7YylqJepEd5MaLPTiWERoZZf\n543rIYRYY2Xm7LcAHgTQAACSJOWhrRcmIV6XmV+Bx9/Jt7RU1lPwylkcX/0Q1ixKwqS4CKxZlIT9\nT89EasJg3SyX2f6C+0pqsWLbcd0SVWTfYIyLHYC9v52J95dOxKxR+qW4v1++hT529Vh4sA1jYyNM\n1xPjFabljS+fmYjPfjXd9ZkSosKxdPJw1TFLJw9HQlQ4IvsGY/rIIXgtbRxC7DaIQzRgy+HzqtIa\nvJIfsph+QV5LCgCAY5VXsKe0Dscqr6hmLMOD9TNoyp6lVly9eQePPTgMQQEM4cGBCLHb8OpjyW6T\nArTXo70lnl4PIcQa09majLETACYCKJIkKYkxNhhAgSRJ9/ryArUoW7PnM8pM7Om0m7hT4wbizMV6\nLBwTjRceHcM9Zu79Q1B++abpbE0rVeh/+O5hHDp3xdS1hwcHmt54Xl5Xjzkb83TjRpmU2tcXVV3D\n+NgB3OMdDU2Y/up+3LwjngXakJaMtBR10OXLbE0AGL3qU9U+wdBAhtI1j3g1O/LFrBPYcuS86/GC\nMdF4ackYytYkpIv4tJQGY+yHAH4MIBnAFgCPAfgvSZJes3idHULBWc+WmV+BVZ+U6MaNGmX3FO7K\nWdgAHF01R3eMtuaXEau1tFJ+vxuXbzab/xAWrufF7BPYcvhuEOHtGmRPby3Etq/F++DMBoLesim3\nDOtyzurGV84f6bWm3R0Negkh3ufT9k2SJP0ZwD8DeAlANYDHOzswIz2faFO5lc3m3ZW7zdhOAP+W\nfVJ3jNTqxM8zj5nKKLRahf6e/sZ7lETcbTwHgJcWj8XejOnYkJaMvRnTvV4cdsOTE4TPyUuhnUmU\nqOFJAodIUdU14bhoKZsQ4n+sJAS8BOCKJEn/KUnSmwC+ZYz91neXRnoj0aZyK5vNuyszm7G//OaK\n7pgmZ1utsV9/WIzJL+8xfL1oMzdvPKuwCjZmtHOLr7HZiTBN1qSoZVFCVDjSUmJ9FihVvrIAaQ/G\nIKQ9xTQk0IbgQBtS7uVnbfqSKFHD0wQOHlHCxH/kluGpLcewtaAaT205hvkbD3jtPQkh3mclIeCH\nkiS5frJKklQDYLn3L4n0ZqLMxJ6+pAmoN2OL3BfZx3VMMOcwdzW5ZidFY1RUmGqMt8k79eU9+PWH\nxSi6cF13jlFRYYY/OIIDGG7eubtnMH3zEaRtOoK39pcjbdMR/Gizvkq+L61c8ADkne2NLU40tXRN\nM3RReRBvLWkC/ISJmaMGodKhnsmkemWE+DcrwVkDYyxOfsAYiwEME6II8Yg2M7G3JAMAd8tZjI7h\nzyQ1S8x1TPLwCO4x7mpy5WTMwPtLJ+LJlGF4f+lEXTJAVmEVam/c0b3uu3EDXMenpw7XPS+70yq5\nSjYUVDhUvSQB4GC5QzeD5kue1vzyBV55EG/TLhcPCQ/hHkf1ygjxX1baN70AII8xtg1AC4A0AL/3\nyVWRXi99SnyvmC3jiewbjAhBJVg7k1zH/PA7sThaeVV3jLYmV1ZhFXaeqFVlHs5OihZm3omCu/DQ\nIMxOikZ5Xb0qG1BLQlsph8i+wcKWRXlll5ESH8l9zts8rfnlK8tnJnZotiwzv8JtT9OEqHDXUvG8\npChsLajWHUP1ygjxX6aDM0mSdraX03gIQAiAJZIkFfvsykiv4648Qk8kl25YnByNjPmjXeMVV/iz\nOsrxJRNisf7z06hRzHLJNbkKKhzIK7uMzCMVuHKrbYlxb+lFrN5xEvMfuAeTRwxE/OBwXUPsORv2\no/wy/70nDW/bzyTadK4k96+cnjgIb+0v1z0vamUEiJt1u2virXx+29Hzqmbwrz6WjGc1fSbNlpbw\nRmN5b1GWmTHb01Reyj6jKZ9CZTEI8V+GwRljrL8kSdfb/34PgGYAOxTP3yNJ0re+vUTSG2hrM3m7\nrII/UjbafjP3HP5w4BzK1i0AACwcE433Dn2je83CMepfqIefn6ubGUvffES3lCi7dtuJrQXVrpkU\nuTjpovFDEffcLsPrjWxfHhNtOleSj5FbFh1UXM+E4f1R6biFAX2CdEG4qFm3uybeyueVPSqVzeCn\nJgwyDO54lHXJOtJY3hsy8ytU9f+Auz1N3c0y52TMwL6SWqpXRkg34W7mbDOAx9v/fghQ9cFl7Y9H\n+OC6SC/CWybbcvg8lqbG9dgZNF6j7WapbTxj/mjcGxnGfR1vfMmEWNdyJW+PlxG5IfbG3afdHhto\na5ud+lN+pdtjI8KCXH//YFmqaybvTG09ckrqUHi+bdJdGYQrm243trdWf3Z7MZJi+nHHpyYMQmTf\nYO7rlORm8MtnJloqxLopt0zYWL4rZtCMysyY2QJgtJRNCPEvhgkBkiQ9rni4UJKkEYo/8ZIkUWBG\nOsyoNlNPJWq0LY97Wu9NtMfLkNOJiiuNbg/77GQdpq7fjw++FO83k5369obqcUp8JBaNuwc5mk3o\nyjZKoo37ou+BvKHfTLNuT2qJdUZdMit6c5kZQnobK9maxmsehHhItExmZvmsuxI12pbHPf1FbLSX\nS0SzUia0/3SdsHuBnqTrj+ouCBdt3I+L7KN7X2UtNTP14TypJdYZdcms6M1lZgjpbawEZ39ljL3D\nGJvHGJsi//HZlZFew6iZdU/0+B8O4j8OnNONMwD5FVdde4i0v4j7KZqLZxVWYdmfjqpqmu0rqcVH\nx6oxOpq/JNoR44f1R4jdXP6QPYDhX/58DKs+KcHRyqtY9UkJkv/1M4TY+Rmo42MHuDbz/25Bkqvp\ntrwfzh4YgOAAddUeZS01d83DPa0l1hl1yazqzWVmCOlNrPTWzIV6zxkAQJKkWd6+KCPUW7Pn6inZ\nmkbZfbxN93GRfXDBcQvKDpZys3e5bMK9A/tg54ka3YZ3oC1DMzzUrmpmPjwiBEseHIbpiYNwrPIK\nsoprkDAoDJ+drG37P7EEJN3TDyc0y4+yhEGheOWxccgru4zpiYMQP7ivYd9PAOgTFIBWp4RFyTH4\nqPCC7vkg1ravTvlDZOnk4Zh470DVZv/fLUzCmHv6uzbuOxqaMGntXjgVL7Qx4OgLc4TZnNpszY7w\np2xNQkj34+vG58PQVtcsBcAtAHsAvCJJUoPVC+0ICs6IP1Nm9wFtMy1ydt/jfziIo+f1wdDwAcE4\nf01frV5u9u6uIbqIspm5lXM8OmYI3k6fpBv/pOgCnt1ejJZmJ1o4r3ty4j149uEkPP5f+TjnuCU8\nf1AA8Pwjo/G9hMGICAty28i9oMKBtE36rgLblqd2Wq00QgjxlE8bnwP4C4BzABYD+AmAfgD+28qb\nEeJPtHuizBI1kDbK7ttXUssNzABwAzPg7uZ/MxveeZQV4K2c40SN/t9bWYVV+OT4t1j18P0Y3J+f\n8fhl5TVE9g1GYIBx45DgwEA8OHwgEqLCsftULVpb1fdMW73fqJCtGeV19dhWUOVKPCCEEH9npUNA\njCRJyo4Av2SM6atLEtINeFLMEwDmbTzgWj7cWlCNUVFhrvZHfzqir0sGAK/mnIXRnnvRzJm8+d/M\nhnfutSoqwFs5hzZZIfXlPa52TntLL6KP3fh1/zzjPvz6Q3F9ark6v6gem7Z6vyeFbGW9sX4eIaT7\ns/LP8SzGmOu3F2PsOwD+5v1LIsS3jIp5GtlXUqva1wXcbSBdXlePOy384MddMmTec3MMs/DcbXiP\n6Rdk2Mw8M78CP888hoVjY1Sb7d/6wXjYNZNcNgC19Xewr6QW+0pq8YN38nV9Nm81639w2BlcHQ6G\nRfThfs6woACE2G1YODYGP3zvMDcws9ugq94vF7JVmpYQ6XZJU1Q/j2bQCCH+zsqes1IAiQBq0dYp\nIBbAebT12WQAJEmSRvroOl1ozxnpqMffyef2pJwUF4GPfi5OQF6x7Ti3R2HC4D4ovyTeYyUyaXg/\nfPTP01yP3fVMVG54P3j2oq5fJq8CvHKGEGjL+Pxg2WRVlXy5hdS1W024dttcXY05o4fggei+utZT\n2pkqpQ1pyfi3HSdR3ySewfvptDi8sOAB7nNyIdvpiYNM7TXbVlCFp7fpZ/A2pCUjLSXW7esJIcQb\nPNlzZmVZk3K2SY+wODmGG5y5qyEmaiBtFJgNCLXh2m19MKLcrC9z1+w9sm+wK6BSdgWQaSvA82YI\nbzQ5caLqKsYpashlzB+N5NgIPLXlmPC9tUID2l6n7AfqriF65uFzhoEZAMw3qGCfEu9+tkypN9bP\nI4T0DKaXNSVJ+sbdH19eKCHe4mkxT7mBtFJUvyDusaNjwvHGE8ko+teHDZccN+WW4eE387Apt4x7\nHkdDE45XXYOjoQkFFQ68vvsMCir47ZmUG9835pTipV0l3ONW7yzB2h0nVWO7NZX73TlWdV035q6j\nQ/EF48Tu2IgQS9dgZFNuGX7xl6/xYGw/1XhPrp/nzqqPj2Pi73dj1cfHu/pSCCFumF7W9Be0rEm8\nxd0yIo9yczwADAoLxOWb+sISezOmq4IA3pKjUdkNQNzMG2jbc/XBslTXY6PlRBEbgHOvLHBdn5WZ\nM6Btr9vh5+e6HpfX1WPOxjxL5+DRfjartPc1OABY+w/J3b5+Xkfw6utVtv+3J4T4lq9LaRDSo6RP\nicdHP59iOjDLKqzSbY7nBWYA8MdD6g4As5OisT5tnGrGTFR2A1A3AdcGZgBwsNzhmkFzt5wo4gRc\nM2iiRutGam7cUXUo4HV68ITys1nFu69NrYCjvrHXBmaimTKaQSPEf1FwRojA2h0nMXndXlcAs/ME\nv1k5z2enjJcJRc2z3/2iAgUVDlN1yeQ6Xx1pEL/1WJXhOUICjWuWae/JS4vHYm/GdGxIS8ajY4ag\nf0iA7odMEAMC3fzk+d0np3R15Mzwt2bl/kD0XXT3HSWEdB0KzgjhGPHcLrx36BvUXG/Ce4e+wYjn\ndmHhWPFmda2HH4gyfF7UPNtxsxlpm45g3aclbuuSyXW+zGxwHz+0L3f8RqMTk1/egzM1+j1kADD2\nHuPZpviBobqxhKhwpKXE4u30Sdj/9EwE2dU/ZmyBNvx/k+81PG9pTT2e2nIM8zce4D4vKiDsb83K\n/YHou+juO0oI6ToUnBGisXbHSWjDIieAUxeuI0aTAKB9LFvz/XGG78Frqq10pOIqlk8bIaxtNm5o\nP+SVXUZBhcPtcqKdAQlDxEFWzY072CGYFWxolgyv80aTcekNbY02uZn5C4+O0dVY45HryCmNW/25\nqqn6uNWfu57zx2blXU30XXT3HSWEdB1KCCBEY/K6vai5rq/YH8CAZ+eNRFT/EF2NsVUfH8dnp+rw\n8B+El50AACAASURBVANRln7pbcotw7tfVMBxs1n3XNqEe7DykSRXbbOKSw3IK7uM3DN1OHHhbiFV\neQO9snH8jqJqVQ0ybb0zrYRBYSi/fFM3vnL+SCyfmYintxZi29f6pUFeSRAeZY02ZYFZucZayvD+\nOFXbgNIafYHYJ1OGYX1a2z3NzK/Aqk/0WahyH1IZNSvX8/Q7SgjpGJ82PvcXFJwRI55kYCrtK6nF\n+s9LcfaiuHZZAIAJcRHC9+BlZhoRNfYG9JmLnjQBL6+rx3Pbj6PgPH/pEgDeeCIZKz8+YZg9On/j\nAZxRdEhQtq5yRxQsKYO2ovNXuRmjygDQqIDwO+kTuQEgIYR0JQrOSK+mnR3qHxJgql+mTNk30yzt\ne2jPwQtgsgqrdDNvP9p8BAc57YwAdeD1+u4z3D6Tv5yVgN/MG6UbN1NiQ1kSw92Mk9XAExCXDFGW\nCml2OvHqY8n4z9wywwBQNHOWNmEodp6oUZ1r0fihpq6PEEJ8ydcdAgjxW0b9Ms3MoPH6ZpqhfA+j\n3ptyIKNtIr7+89M4/PxcfLAsFb/9sAjbCy/o3iOv7LIrOBs3rD/3OnjjZkps/OOkYVj32N0lruUz\nEw2XAbVdCNwRlQzZmFOKTV9UorHZicb2HX7Pbi/GoRWzUHT+qjAATJ8Sj9d2n9G1pNp5okZ3rqkJ\ng2gGjRDSLVFCAOkRsgWlEkTjWqIK+cn39OOOK/3xcKXhOeRxXp00Za2wf5zE7/coZ2Vm5ldgw56z\nuucZgEHh6ur65XX1eO/gOd2xWk99b4Tq8cacUsx4LRcbc0rdvlZJ2clASVTCIru4VlcqxG6zofrq\nbV1NOK3jqx/CmkVJmBQXgTWLkvDBssnCcxFCSHdEwRnpEUR9Md31y5TNS+KXFfjVHOOsSqCtt+b8\njQeE55DHRXXS5PGU+Ehuq6eU+EhXhiJvw3yw3YZhEXdLWryYdQJzNuZx+4AaSVy5C2/mnkOl4xbe\nzD2HxJX6qvI82UUXMHX9fqRv/hJT1+/HJ0V3Z/9EJSwWJ0friuvWN7WoPocRZQHhYRGhurIjzU6n\n6XMRQoi/6ZTgjDH2BGOsgjFWzhj7ieCY9xlj+s00hJhg1C9TOauj7D+pxOubKffALF3zCFbOH4nR\nMeJyFGfqbuJQ+WXERaoDgrjIUFy91YzyunphnbSG283YV1ILR0MTvrminu355sptbMot42ZaBjG4\nSlPIy3dWuwX8246TeHprIRKf24VmzfbTZgm6GTRtfTFtJ4PGZiee3V7smkETlbboE8TfUfHbvxSa\nvnaZqFyHdklT+T3YV1KLFduOe1TolhBCfM3nCQGMsXAAJQBSAbQCKAIwVpKkS4pjZgJ4GsAoSZIS\njM5HCQFES87Q/OZyAy423C1JMSoqDP88M9G16byhqQXKb/vSycPx0uKxqnOJNrxnFVbhDwf+jrMX\n3e9LmzlqEIaEh+DijUbknr2ser89p2pRo1nalA2PaAvklDNK4cGBGDYwlDtjNjomHJlPfdcVhDga\nmrBxz1lkfmm9lZNIXGQfHHhmJgB+wsWWp1KRvvlL3TVnLvsuximK42oTDR5+M4/7mQB9lqhZonId\ngHGfUitZp4QQYpW/JgTMB/A3SZIuAABjbD+A2QD+0v44BMAaAL8A8GEnXA/pQYzqd52pu4nfbi1C\niwTXRnGlLYfPY2lqnKrnIm/Du7bZuTu5Zy7j/aUTdWUhthw+j70Z03HywjX896FKFF+4oXr+/NXb\n0K6gNjudWJIcww1kfjgp1hWEZBddwDMfFaHFuCasZd+7byAAccLFkfJLppYUtYkGos8E3O0xarU+\nWWTfYG4CgHJ2j/c90CZtEEJIV+uMZc1YAN8oHlcDUG5EeRHAHwBcEZ2AMfYzxlgBY6zg0qVLosNI\nL/L01kLcv+pTw8KqANDiZmLYXV9K3iZ+Mz46VsUdzzlViyUTYjE6hp9oMCluoG55bvnMROGSLdAW\nfPz6L0W40wpO6NEx44e3BWf/eeDv3Of3nrlkaklRy12HBG/2wjTTp1SUzEEIIV2hM2bOgqD+neFE\n2/ImGGNjAYyTJOl5xlic6ASSJL0L4F2gbVnTZ1dKuoW458xtVDfDXV9KK83OlVpajcOkeUlR3A37\ny6bFY/zwCN3y3PHVDwkL7B7+uwO++j/F+NgBSFyp348mq7txGzdu3cGhFbMsF4AtXfMIfrz5MA6U\n6/9d5s1emLyEAS1RMgchhHSFzpg5qwGgrAY5DIA8rfBPABIYY0UAPgUQyxjb2gnXRLqQqGm1GU9v\nNb9hPDw4wPD5aQmRqiVNrfK6egzuy++d6U5gAP//WvMfaFs64yUgRPcLwv8crcLBsxcxLnYAIvsG\nq0pbKDMUlS43NLq9HnlW660fjNe9r8jSycOxo6haGJgBwPkrjVj1SQlmbch1XbMVf1w2uUO9MM1s\n7NcmDGjJiR+EEOIvOiMhIBrAMQAPoi0YzEdbQsBNzXFxAPZSQkDP1tEq/u56RIYGAovGD3PNhPDa\nAclC7DYcWjGLG1AYVdbvY2e43SwZzla9v3Qi/lZ2CVsO3z2HUQJCzqlvce323dmdmH5BuFx/RxUY\n2RlQtm6B7r3K6+oxZ2OewdUAj08YiuceGe36rMrEh89OfIs9pRcxd/QQ/HxGoqs/Z0JUOGa8lotK\nh7iVlZK2v6UVnvTCNNONQUnbKspqpwNCCPGEXyYESJJUyxh7AcDh9qHfApjHGLtPkqQNvn5/4j86\nWsUfAB4c1p+7DNY3CHjuIX1wMCoqTNUOSEkuVLrt6HlVYCAqRzF5xEA8mTIM00YOwdT1+9HYzF8q\nk2diZidFY2lqnCrY0ZqdFI36xmbdEicvo1MubZExf7RqPCEqHEsnD1cFglpHv7mqCkKViQ/a4ER5\nnYuTo/FmrvtitkBbwV9PgzN3nQm0zHRj0FImDFjtdEAIIZ2pU+qcSZL0R0mS7mv/89f2Pxs0x1S6\nmzUj3VtHq/gDQHL7BnWtn3wvgRsY5GTMQNqDMQgPtiGAkwn55DuHsC7nLEpr6rEu5yxGr/pUmCTw\n2IRhWDIhlltX66ffi8eTKcPw/tKJqtmbiLAgJEaFIyJMvDxqZV9bdjH/2JcWj8XejOmYff8g7vOL\nkz0LRDLmj4bduAav4j28t0/MHXfdGAghpDuj3pqk0yxOjsHRyqvccbOmJw7iNv6WWxxpaZtuA237\nr5qdTswfPUQX7NxukXCm5jr3XMrkgUXjhyIpph93VkxePjt54Tp+v6vEbTPuhWOjsbf0ovhDKxgF\nWQlR4Vj5cBL2ndYvcT46fpip8/OUrVuAjTmlyC6uxeLkaGTMH61LErAzeDxr5glRQgVt7CeE9AQU\nnBEAQEGFA3lllzE9cZCryba38ZpWK0tCmJESH4lpCZE4WO5wjU1LiHRdszKj8WZTiy4wA4DvP3gP\nfjlnJNLf/5L7Hh8eq8bMUYOQe0ZdQFYOwPaV1OK9g+dQeP4aggMDVIFXdtEF/HprEZRbOeXaWk9/\ndBxJMf10y5tLJsRi/eenVUuZkX3suH67WVUKxM7gWtIUFVwVzfoVVV1TvW95Xb1hYKk977SRQyAx\nG6YlDkJ5XT23m0B5Xb1hgoU3yQkVZzR7zmipkhDSE1BwRpC++Qi+aA923tpfjmkJkfhgWapP3suo\nJIRZaSmx+LLiKgAJAMPjKW0Nw5XJArwZOtlX7fuvRIVQrze2IvdMWyum/zMzURXAaDeh32ltqzb/\nzLZiVF25hdd26xuT3z1WwiNvf4ENafoZtMPPz0VWYRU2f1GJU9/eQENTK2w2YN7IITh7scE1YwWo\nq91rZ+REpUGU49pkBzlRQXRe7fcjcTA/21MbAPpaTsYMYUcHQgjpzig46+UKKhyuX7yyg+UOFFQ4\nfDqD5ukSmFzt/Y6rjpiEZ7cXo+bqLbcFaWVyDa3lMxPxxr4y7uwaAFQ6biOij101Y6bdhC5ranEa\nBmayOy1OPP1REa7cbML3Egbjj4fO4bNTdXj4gShkzLsfGR8WQwLQ1P759pReRMGqOaoWTdpq989u\nL8bUhEGI7BvMTQ5Qzvrxkh22HD6PRcn3cM8bFhSg+36UXeLfA3c143yBNvYTQnoiCs56ubyyy8Jx\nUXDW0ZmvjpCrvSvb8DAAf/gbv4K9VkgAkJowGI6GJhSdv4omNy0E/udoFcYPj8C2o+dNv4c7d1qB\n1TtKAdxtKp75VTUyv9LvoZLQVmR24bh7APA/v5x1KgdwLy0eK8wSFS175pVd5p5XtMF+7NB+OKFo\nP6UMAAkhhHQMBWe9nNUN9tqlw9d2n7FUpwwA1u44iZ0na7FwTDReeHSMpdfyqr3fbnbidjP/+EFh\ndly+effJplboGnUb+eLsRUxcs9fSNXqbssjssIhQ3bXXN7XoelkmRIVzgyXR7Nb0xEF496C6ZEaz\n0ynceP+vC5MwoE+QYZkQQgghnumUUhrEf8kb7JWUG+yVjOqUmTXiuV1479A3qLnehPcOfYMRFlsx\nKctYhNqNv7597EwVmAFtM1FmAzMAMLlS6lPfSxjs+nvRef5eOtG4lrzsqbR08nCkxEdye2TOTooW\nfj8SosKRlhJLgRkhhHgZzZwRfLAs1VS2plGdMjPLm2t3nNQ15na2j1uZQVs0fiimJgzCiu3F3BIU\nkWF2ZMxOxIlvb3BnfboT7XKhUX0vs3uvRMue8n3VZmua/X4QQgjxDpo5IwDaZtB+M2+U4S9eUT0y\ns3XKdp7kF1AVjRuJ7BuMf5wUy33u1ceSkT4lvtvXvPrVzBG6dk+iz2T1s4pmvSL7BnN7ZJr5fhBC\nCPEOCs6IaelT4tE/RN1M3EqdsoVj+DM7TqckXBpVNv7Wmp0UjZh+6sr7Mf2CMDspGv970yEszzyG\nEE7vc7u2VUAXiB0Q4vaYU7UNujFew3Sq70UIIT2Lzxufexs1Pu96vGxNs/WmRjy3S7e0KdM2QedV\noVc2/nY0NCFlzV7DBuSyJ1OGoX9IIP505DwCbAy37pjbTPbCI/dj7aenTR3LE2gDWp1QXaMNwIfL\nU5G26Yjha994IhlLJvBnB6m+FyGEdA9+2fic9DzaOmXKwqxbC6oxKipM1V9S6dwrC7B2x0n8T8F5\nNDSpwyplE/SNOaXcKvTKxt9/Law2FZgBwLmLN3CipgFNLaLQkO/B2AEIsDG0Oj37RwxjDAWrZuOd\n3DJVhqqjoQk2BhiddtrIIcLnqL4XIYT0XLSsSTqEV5j1TN1N7CsR7yP7+cxExA7sy31OTjoQNfhW\njn92wnzD9MKqG7DbrH/df/GXrxFoUy+DWlkUDQkMQPXV23jh0TE4vHKOK/Gh+upthAWJ/20UHhyI\n6qu3LV8vIYSQ7o+CM9IhRtmDPNlFFzB1/X6cE1SZl5MLRA2+lePNFmazJsT2s1RCQ1ZzvUk322Zl\nDq3Z6dTVIAP49dqUeLXLCCGE9A4UnBGVggoHXt99BgUVDuEx5XX12FZQhfK6ekvZg8rWQ6LlxYeT\n2yrhZ8wfDbtmikrZ+BsAgjmb/UVGRfczf7AX2G0Mdltb5qg287G8rh65py/iN3NGIsRu4yYtAOZr\nlxFCCOlZaM8ZcTHTAJ3XNHtUVBjOKJY2RdmDvNZDSvJSnhzMlK1bgI05pcgurlU1/gbakhKOnb/B\nPY/SlPgB+L/Lp2LC73PcHttRk+Ii0Op0ovD8ddes3kcFVaom59r790TKMFy5eYdbr81s7bLyunpd\nzTJHQ5OuXhkhhJDugYIzAsBcA3RR0+y9GdPxjeOm2+xBd0t5vCXAjPmjVUEZoG4h5c6kuIFt/zt8\nIHI4AZA3zRk1GOty1M3PlfeQd/8+LKjGvPv5rbJaW91/Rl6wPPHegVixvRh2mw3NTidefSxZFSAS\nQgjxb7SsSQAYN0CXiZpmF1Vdw+ykaKxPG6cLzJRLoADwLzMSEGhjsDH9xvpJ90boZnlWfXwcE3+/\nG6s+Pg6A30LKSHZxLcrr6jE5gR8AedPNZn7gmVd2GeV19XhP07tSdqKmnjt+qrbBcJlZFCw/u61t\n6bi+qQWNzU48u70YjoYmi5+GEEJIV6GZMwLAXAN0UdNs0bh2Vsdd6YiD5Q6U19W7lubiFH03M7+q\nRuZX1ZgUF2H4ObT6BtkwZ2Oe6eMjQgPwv0YNQVaR+UxQ2RVFg3KlM7X1mLNffA0Lx0TjvUPf6MZv\nN7W4aqHxlplFwbI26LXbbKrlYkIIIf6NZs56sazCKiz701FkFVaZaoAuaprNa3zNm9Uxk1wpBxzy\nTJmO01on8pM1+ir7IvcP6YOv//Uh/G7hAx51Ecg5xc9QzRFkrgJt9++FR8cgNFD9fkEMqLyiLqUh\nL5HKREGx9jaLMkYJIYT4J5o566VSX96D2ht3AAB7Sy9i/eencfj5uW4bXH90tEr3WNv/ERDP6rgT\nYg+Ao6EJnwkCnXOO2/+vvXuPjrq88zj+/uYCQQgiCSZZCCYSVFgSU4xdLpWKoNRKgdXWurts2t3V\n1t2e1XVrkarb7XqoRepW3e7xrB7tUWrXKrIFhVppAa9Ft5HGYLFKVtCABBFFgRIIybN/zEyYy28m\nM0nmlvm8zvE088zML0+e/jL58ly+X04tyk9oaTNe733im/na/dFRigry6exKPPVGvL7cMI5rLjiz\nJ7B9Y9nnuW/zDta07GVRXQVHOrs9ZzKf3/FBz/8vgWB55ZbQPWcNZ4xmSdieM82aiYhkDwVnOWjN\n1raewCxg7yfHWbO1jUVTK6MWt75v8w6Ongidlzl6wnHf5h18ffbEkPZoszqx5OcZ3/6fbXR2d3Ne\n5am89HZkKolL/7SMZZefyyO/2cnPmtp4/T3v/Vp98UlHN9Nv/xXrrpsVcXChwGD+uRV8oe5P2Hvw\nKLc+uT3i/Vd/pjriQEA0wYFZwNdnT+wZx6adB3pdZga4bWEtjdOqIk5rzqwp1WlNEZEspWXNHLRu\nm3f2/WjtAWtavPdhebV7LYFG840Lz2RoQR5d3a5nE/urbR97vnbZ5ecCvhJS666bFff3iNfeT47z\nwlvvs+KKOooK8ygeWkBRYR4//HI9d181lTmTy1k8o9qz4PrXZ0/0XPaNdyk4WDzLzAE1ZcV8saEy\n5JolI4ZybuUoBWYiIllIM2c5aH5tuWderfm1sXNqLaqr4A2Pk4WL/Fn9AwI5tn4ZZ3ml/Lw8huTn\nhSSmLczL47FvTGfVb9/h6d/v65kxCxc8c3TjEy1xfb+/PH8cVaUjuP1p74Lm//rk7/nWJWfz0k0X\nec4+HTh8jP/66/PZ1vYRz+74gPm15T0FyqPNZHm19eYnV0/rdZlZREQGHwVnOWjR1Eru+OUf2Bu0\ntFkxckhPgBHNef6cYbHa1zbv4abVLeCgI84i47MmlnJ/WJqJo51d3LiqmXPHjmTupDJmn+NdiQB8\nM0exankGqxg5hG/OO4eZd2yK+pqPO7q49cnt/GDDm7z23c8BJxO9fnjkON9/+g84fKci77mqPiKH\nWE1ZcUQA5tUWj4Zq79kyEREZvBSc5agtN1/Mmq1trNvWHjLzE0usXGgN1SUh5ZmiOX34EN4/cjIo\nbJw+nobqElZcUce3nniNfMvjj51dnOh27Hj/CDve91UeeKxpN2eXDeeZGy6MuOZdz7zBfz7rnUMs\noGr0MP5p7kQWTa3ktbaDMSsVBHzc0cVXHnyFosJ8zxOXDrjuZ83MrCnV8qGIiAwY7TnLYYumVvLA\nV86PKzCDyM3o4e27PzrK8V5my+ZNKWNoQR5FBXkMLcij4QzfrJvvmIHFrCDw5r4jETNkE7+9nns2\nv01XL2k69h8+xpSxvkMK404bxtHO+E5iPrfjg5ipMADu/lV8hwBERETioeBM4hZrk/qarW3csrql\n11xmj7+6h2Mnuuk44St+fuOqFm75+Ta+taqFYye66ewlytoQFCjd9cwbdMaROw3gyPFu5t71PN9Z\nuw0As8TzmEXzy9ejL6keOHyM19oOKkO/iIjETcuaEpdADq59H4cmRn3/UEdIzrRYDHBhwdTxrm5+\n+sq7nq/3csnkk3vP1rbEt88s2Mot7/LpqpIBzWM2pMD73ziB/XeqcSkiIolQcCa9mnTrLyLymwW8\nue9I3Ndx+IKxvqoYOSSkdufCunLu2Rx7r5mXDw4fi8xjlmeciKeEgYe//+yZEW3B++8Ce9uWrG7R\n/jQREemVljUlJq/Es8mUF2O18aOjJ0KWB2+YN4nCPqxOfqamNDKP2ZWRaTqCTR1/Knd+sY7hQ0K/\n4alF+SyeUR3x+t0fHaUwL/TXK1DjUkREJBbNnElMT/xuT0q/37zJZVFLN3kV8L7zy/V88/HXos56\nfapyJL9r+6TncSABbE1ZcUgW/adb3vN8/2cnlvKPF9X0pLP4YkMlj/xmJ2tb9rKwrsIzMAPfoYPw\n2TnVuBQRkXgoOJOYZpxZ0pPOItkuqClh2Z/XRg3OwoObwNJhtMBsWIHx829c0JOjLDwBbMmIoT2B\n3too1Q/+2NkVkWds8YzqqEFZ8LVXXFGnGpciIpIwBWcS03Vzz+Lhl+PfsN9X3553Vk9dyf+4qp5/\nfqyZ8NXUjs7ukOAmsHQYnK9saEEeFacW8RcN43quF08C2IV1Ffx2V2Qtz4Vh1Q8SsaB+rGpciohI\nwrTnTKL63lOvM/9HLzC5fHjSv9f3n3mLeXc9C/iCmsrTijxfN/fOk5n9vZYOAb46oyrk4EA8Fs+o\n5tSi/JC2aPvJEpFIjcuN29u56YnX4q52ICIig5O58NwGGa6hocE1NTWluxuD3plL1/eSPz85Hmw8\njzmTy6lauj7qa3Ytv6zn6yeb9/QsHf6xs4uuoCXOxunjuW1hbULfP579ZMlwyV3P8lbQyddo1RBE\nRCS7mNmrzrmGRN6jmbMcFi1B6veeej0tgRmcTDJbU+q9cT68fUH9WF666SK+f3ltSGAGvpxmrfsi\nC7XHsnhGNauunZHSwGzj9vaQwAy8qyGIiEhuUHCWo9Y272HmHZtY/MArzLxjE082nzyVuS5Gxvtk\nCySZfezamZ7P//rGiyLaSkYMpaOzy/P1zW0HB65zSbIhSnmoaO0iIjK4KTjLQcEJUg8dO0FHZzdL\nVrf0zKDNPHN02vp2Rslw1jbvYcbyTRHPFRXmRS2DVF85KqH2TBJc9SCedhERGdwUnOWg3hKk7j/U\neymmfIP8sIyxA1Gt8sXWD7hpta/OZrhYSVxryoppnD4+pC2Q0yzTzZlcztlloYcuzi4bnvChBhER\nGRyUSiMH9ZYg9bnWA71eY+ml53D51HF8dOR4Tw6xf3vqdV5o/bBffSuNcaqxtySuty2spXFalWdO\ns0z3zA0XsnF7Oxu27+OSyWUKzEREcpiCsxzU3wSpZ5cN55pZE3quFQiC/vULU5h71/N97lfj9PGc\nU15MR2fkrNmQfOLqYzw5zTLVnMnlCspERCQ1wZmZXQncAXQBtzvnfhz03PXAtcApwPPA3zjnTqSi\nX7ksWoLU+zbviPm+62efyQ3zJnk+F1haXLkl/qS1C86tYNbEMT0zXete8y6jNPus01lQPzbu64qI\niGSrpOc5M7NiYDswDV9w1gzUOuf2+5//W+ARoBv4BfCwc+6n0a6nPGfJM+nWX8RV5Ly3HFyt+w7x\n4Itv8+hvd0d9TWG+8ejVfxZRGumhl3by3ae2e75nWIHxxrLP99o/ERGRTJGpec7mAc855/Y459qB\nTcCcwJPOuR875477Z8tagPQdFcwxwXnO7tu8I67ADHrPwVVTVsz+w96HCvLzjKLCPP79S+dGBGYA\nn6kpjXrdoydcrzN7IiIi2S4Vy5qVwDtBj3cDEQULzewU4DLgUo/nvgZ8DWD8+PHhT0sfrG3ew01B\ne86K8hM7a7lh+76Y+6Pm15bz6zfej2i//qIJ/NW0qqh7x3pbGl3TsrenZqaIiMhglIqZsyEQknC+\nG9/yZg8zywNWAj9yzu0Kv4Bz7n7nXINzrmHMmDHJ7GtO8MpzdrDDO4lrNL3l4Fo0tZKKkUNC2ipG\nDuG6uWf3uqn/toW1XDPzDO/r9qMQuYiISDZIRXC2FwjeyT0OaAs8MDMDHgC2O+fuTUF/cp5XnrNE\neOXg8iraveXmi7n7yjrmTjqdu6+sY8vNF8f9PW75whSGFYTO5g0rMM2aiYjIoJeKAwHlwKvAp/AF\ng7/BdyDgiP/5/wI+dM7dHM/1dCCg/w4cPsZ5y34d8zUTxpzCRWeNYd3r7cyfUs60CaVRc3Als2j3\nfZt3sKZlL4vqKhSYiYhI1unLgYCkB2cAZvZV4F/8D2/0/+8E4GV86TPeDnr5vzjnHo12LQVn/XfX\nM29wz+a3Y75m2YLJcRX/3ri9nb9b+WpE+4ON5ylnl4iI5Ly+BGcpyXPmnHsIeCjK0yohlWJrW2IX\nNjegtvK0uK4Vq2i3gjMREZHEKTDKQQvrYgdNQwvzYpZJCqai3SIiIgNLwVmO2bi9nfZDxz2LlBcP\nLaCoMC+hUk4q2i0iIjKwVFszh4Rv3B99Sj4jhw1lYV05jTPPjCjlFC8V7RYRERk4Cs5yxMbt7SGB\nGcCHf+ziB1+c1BNMJRqUBVPRbhERkYGhZc0MElxOaaDF2rjfH637DvFEUxut+w716zoiIiLio5mz\nDBFeTmnFFXUsqB/b+xvjdMnkMh5riixE3p+N+99Zs42VL58ss9Q4fTy3Lazt8/VEREREM2dJF89s\nmFc5pSWrWwZ0Bm2gN+637jsUEpgBrNzyrmbQRERE+kkzZ0kU72xYoJxSR1AJ0sK8PHZ/dLRf+8DC\nDeTG/ea2g1Hba8qK+3xdERGRXKfgLEmCZ8MCQdeS1S3MrCmNCLjGnTaMzu7ukLbO7u64c40lYqA2\n7tdXjkqoXUREROKjZc0k8SouHpgNC1cyYigrrqijqDCvT7nG0qGmrJjG6eND2hqnj9esmYiISD9p\n5ixJEp0NW1A/lpk1pX3ONZYOty2spXFaFc1tB6mvHKXATEREZAAoOEuSwGzYkrA9Z7GCrpIRx7DE\nfQAAC+dJREFUQ9MelB04fCyhALGmrFhBmYiIyABScJZE2TYblux0HiIiItI7BWdJlorZsNkrNrLz\nww6qRxexecmcPl0jkQMMIiIikjw6EJDlqpauZ+eHHQDs/LCDqqXr+3SdRA4wiIiISPIoOMtis1ds\nTKg9llSm8xAREZHoFJwNoNkrNlK1dH2fgqO+CMyYxdseSzam8xARERmMtOdsgAQvJwaWF3ctvyyp\n37N6dJFnIFY9uqhP18u2AwwiIiKDkWbOBsBALi8mItrm/74eCgDfDNq5laMUmImIiKSJgrMYWvcd\n4ommtl6LeQ/k8mKidi2/rGemrHp0UdJn60RERCS5tKwZxXfWbGPly+/2PG6cPp7bFtZ6vjba8iLQ\n6/LmQKTB6M9MmYiIiGQWzZx5aN13KCQwA1i55d2oM2i9BUdfuvcFz/aBSoMhAr5cda+1HeTA4WPp\n7oqIiPSDgjMPzW0HE2oHYs6ObW37JKItkX1q05ZtoGrpeqYt2xD1e0huW9u8h5l3bGLxA68w845N\nPNm8J91dEhGRPlJw5qG+clRC7QHnjx/p2T61MrI93n1qVUvX0364E4D2w52aXZMIwdUdDh07QUdn\nN0tWt2gGTUQkSyk481BTVkzj9PEhbY3Tx/da4HvVP1wQd3u0dBfB7dFmyjSDJsFU3UFEZHDRgYAo\nbltYS+O0KprbDlJfOarXwCxg1/LL+NK9L7C17ROmVo6MGrBtXjLHcxYseP9aYMYsXLR2yU2q7iAi\nMrgoOIuhpqw47qAsWLSALNyu5ZfFPK1ZPqLQMxArH1GYcJ9k8ApUd1iyuoXCvDw6u7tV3UFEJIuZ\ncy7dfUhIQ0ODa2pqSnc3UsZrdk25zMTLgcPHVN1BRCTDmNmrzrmGRN6jPWcZbtfyy3pmyspHFCow\nk6hU3UFEZHDQsmYWePnWS+J+rWZPREREspuCs0FkbfMebgrbd7Sgfmy6uyUiIiIJUHDWB9OWbaD9\ncCflIwoTmtVKpuBcVx34Tu4tWd3CzJpSzaCJiIhkEe05S1CmJoVVrisREZHBQcFZAjI5KaxyXYmI\niAwOCs4SkMlJYQO5rooK8ygeWkBRYZ5yXYmIiGQh7Tnz0LTzAM/v+IBZE0tpqC7pac/0pLAL6scy\ns6ZUpzVFRESymJLQhln8wMu82Hqg5/EFNSX85OppPY+VFFZERETipSS0/dS080BIYAbwQusBmnae\nbFNSWBEREUkmLWsGeX7HB1Hbg5c3MyV9hoiIiAw+mjkLMmtiaULtIiIiIgNNwVmQhuoSLqgpCWm7\noKYkZNZMREREJJlSsqxpZlcCdwBdwO3OuR8HPTcF+CkwCngSuN451+15oRT4ydXTop7WFBEREUm2\npAdnZlYM/DswDV9w1mxmTznn9vtfci+wFNgAbAIWAGuS3a9YGqo1WyYiIiLpkYplzXnAc865Pc65\ndnwB2BwAMxsDVDvnnnbOdeGbQftcCvokIiIikpFSEZxVAu8EPd4NVPi/Hge8G+U5ERERkZyTiuBs\nCBC8h6wb3/Jmb8/1MLOvmVmTmTXt378//GkRERGRQSMVwdleYGzQ43FAWxzP9XDO3e+ca3DONYwZ\nMyZpHRURERFJt1QEZxuAeWZ2upmVAzP8bTjn3gWOmNmFZpYP/DWwKgV9EhEREclIST+t6ZxrN7Nb\ngC3+pm8Cl5jZBOfcncBXgIfxpdJ4yDn3YrL7JCIiIpKpUpLnzDn3EPBQlOe2ArWp6IeIiIhIplOF\nABEREZEMYs65dPchIWa2HzgCeFcpz12laEzCaUy8aVwiaUwiaUy8aVwiaUwiBY/JGc65hE4zZl1w\nBmBmTc65hnT3I5NoTCJpTLxpXCJpTCJpTLxpXCJpTCL1d0y0rCkiIiKSQRSciYiIiGSQbA3O7k93\nBzKQxiSSxsSbxiWSxiSSxsSbxiWSxiRSv8YkK/eciYiIiAxW2TpzJiIiIjIoKTgTkZxhZsPM7Kx0\n9yOTaEy8aVwiaUxSJ6uCMzO70sx2mlmrmf1tuvuTLmZWZGb3m9lbZvaOmd3gb+/0j02rmf0s3f1M\nNTPbFfTzv+Bvu97M3jWzN83s0nT3MZXMbGnQeLSaWYeZfT4X7xMzG2lma4B9wJKgds/7w8yWm9lu\nM9tmZuelo8/J5jUmZlZiZo+Z2Q4z+z8zu8rfXmVmR4PumzvT2fdkinGveP7e5PC98qOwz5cuM5uc\nK/dKjL/DA/OZ4pzLiv+AYqANGAuUA+3AmHT3K01jUQJcARi+RHf7gEpgV7r7luZx2RX2eALwlv/e\nmQy8BxSmu59pGptTgbfxlWzLufsEGAHMAa4GHoh1fwAXAS/6x+pioDnd/U/hmJwDXOj/ugY46B+T\nKuDZdPc5XePib9/l8dqcvVfCnj8LaPJ/nRP3SpS/w58dqM+UbJo5mwc855zb45xrBzbhu1lyjnPu\ngHNutfP5AF/QOird/cpAfw487pw75JzbDuwCBuW/bOPwV8ATzrkT6e5IOjjnDjvnNgLBP3+0++Ny\n4CHn3Ann3K+AMWZWnvJOJ5nXmDjn/uCce9b/dSvQCQxLTw/TI8q9Ek3O3ithrgZ+nMIupV2Uv8Oz\nGKDPlGwKziqBd4Ie7wYq0tSXjGFmU4Ai4HWgxL8UsdnMcjFb81H/z/+ymc1D90ywv+Pkh2eu3ycB\n0e6P8PY95OB941+S2eqc+wRwQJ3/vllnZjVp7l46eP3e5Py9YmaFwJXAf/ubcu5eCfo7XMoAfaZk\nU3A2BOgOetwNdKWpLxnBzEqBnwB/44/ei51zE4B7gZ+nt3ep55yb5P/5vwX8FN0zAPj3N3Q45/4A\nkOv3SZBo90fO3zf+P6g/AL4O4Jx7xzk3GpgIbAYeSl/v0iPK703O3yvAAuAl59xByL17JfjvMAP4\nmZJNwdlefPvNAsbhm0bMSWZ2GrAOuNk599vg55xzq4BhZpaTS53OuRfwTSfrnvG5BngwvDHX7xOi\n3x/h7X+C71/AOcHMzgCeABqdc7uCn3POdQP3AVPS0LWMEPZ7k9P3il+0z5dBf694/B0esM+UbArO\nNgDzzOx0/1rtDH9bzjGzkcBTwDLn3NP+ttLAH1n/csSBwL9kcoGZDTezCv/Xn8I3ZbwRuMrMTjGz\nycBooDmN3Uw5MxsOfAF43P84p++TMOvxvj/WA18xs3wzuxh4yzn3YTo7mipmNhb4H+Aa59zW4HYz\nC+w9Wwz8bzr6ly4xfm9y9l6BnkB+Ar4ZskBbTtwrXn+HGcDPlIIk9n1AOefazewWYIu/6ZvOuSPp\n7FMaXQd8CrjbzO72t10BrDGzbnxR+pXp6lyanAI8Z2b5wMfAYufcS2b2CPB7oAO42vmP2eSQLwO/\ndM4d9j+uAJ7MtfvEzIqB3+E7RVVkZhfi+xd/xP1hZj/Hd+rqbeAA8Jdp6XSSRRmTwMmzR80s8NLJ\n/v8eNLNOYAe+sRuUoozLPcA/e/ze5PK9cg0wG1gZ9rmaK/eK19/hSxigzxSVbxIRERHJINm0rCki\nIiIy6Ck4ExEREckgCs5EREREMoiCMxEREZEMouBMREREJIMoOBMRERHJIArORERERDKIgjMRERGR\nDKLgTERERCSDKDgTkUHPzP7bzP7PzFrN7CIz+66ZPWxmr5vZ35tZiZmtNbM3zWyjv34vZrbC/553\nzOwv0v1ziEhuUPkmERn0zGyWc+55M5sP3Ag8C1wOfBo4BjwMPOqce9rMrgUmOeeuD3rfFGCdc64q\nTT+CiOSQrCl8LiLSDwVm9kOgHhjrb/uFc64DwMwuBT5tZnfh+1xs9r/mqJktByYFvU9EJKkUnInI\noGZmnwNuB64FHgEe9z91OOhlhcD5zrlDQe/7U+Ax4KvAD4H3UtFfERHtORORwW4K8Ipz7n+BuVFe\n8yLwDwBmdrqZTcY3W/amc+554MJUdFREBBScicjgtwq4wMzeBE6L8pp/BOab2S7gGWAIsAE41cze\nBianoqMiIqADASIiIiIZRTNnIiIiIhlEwZmIiIhIBlFwJiIiIpJBFJyJiIiIZBAFZyIiIiIZRMGZ\niIiISAZRcCYiIiKSQRSciYiIiGQQBWciIiIiGeT/ATABM/Do6SDOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc64ab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "df[['price', 'area']].plot(kind='scatter', x = 'area', y = 'price', figsize=[10,5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析價格與平米關係"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y= df['price']\n",
    "X = df[['area']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regr = LinearRegression()\n",
    "regr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient:[ 64846.01038065]\n",
      "Intercept:-9165.217457327992\n"
     ]
    }
   ],
   "source": [
    "print('Coefficient:{}'.format(regr.coef_) )\n",
    "print('Intercept:{}'.format(regr.intercept_) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xdedb9b0>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X5004WIp163In4qVxc66GMpgKCJu/A/ge8PGs5x8CbiwlF0g5D8vdVNva2orn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\nZeRBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc6b83c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X,y, color=\"blue\")\n",
    "plt.plot(X, regr.predict(X), linewidth = 3, color = \"red\")\n",
    "plt.xlabel('area')\n",
    "plt.ylabel('price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 多元迴歸預測"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y= df['price'].values\n",
    "X = df[['age', 'area', 'room', 'living_room', 'total_floor', '东南向', '东向', '南向', '西南向', '西向', '中', '高']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regr = LinearRegression()\n",
    "regr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2792 entries, 0 to 2791\n",
      "Data columns (total 12 columns):\n",
      "age            2792 non-null int64\n",
      "area           2792 non-null int64\n",
      "room           2792 non-null int32\n",
      "living_room    2792 non-null int32\n",
      "total_floor    2792 non-null int32\n",
      "东南向            2792 non-null uint8\n",
      "东向             2792 non-null uint8\n",
      "南向             2792 non-null uint8\n",
      "西南向            2792 non-null uint8\n",
      "西向             2792 non-null uint8\n",
      "中              2792 non-null uint8\n",
      "高              2792 non-null uint8\n",
      "dtypes: int32(3), int64(2), uint8(7)\n",
      "memory usage: 117.2 KB\n"
     ]
    }
   ],
   "source": [
    "X.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 評估迴歸模型 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.851\n",
      "Model:                            OLS   Adj. R-squared:                  0.850\n",
      "Method:                 Least Squares   F-statistic:                     1319.\n",
      "Date:                Mon, 05 Jun 2017   Prob (F-statistic):               0.00\n",
      "Time:                        00:05:02   Log-Likelihood:                -42320.\n",
      "No. Observations:                2792   AIC:                         8.467e+04\n",
      "Df Residuals:                    2779   BIC:                         8.474e+04\n",
      "Df Model:                          12                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "-------------------------------------------------------------------------------\n",
      "const        1126.6698   1.06e+05      0.011      0.992     -2.07e+05  2.09e+05\n",
      "age          9389.0503   2463.360      3.811      0.000      4558.849  1.42e+04\n",
      "area          6.99e+04   1246.879     56.057      0.000      6.75e+04  7.23e+04\n",
      "room        -3.606e+05   4.34e+04     -8.308      0.000     -4.46e+05 -2.76e+05\n",
      "living_room -2.297e+04   5.63e+04     -0.408      0.683     -1.33e+05  8.75e+04\n",
      "total_floor  1.826e+04   2773.446      6.584      0.000      1.28e+04  2.37e+04\n",
      "东南向          5.026e+05   2.51e+05      2.002      0.045      1.04e+04  9.95e+05\n",
      "东向          -5.405e+05   1.72e+05     -3.133      0.002     -8.79e+05 -2.02e+05\n",
      "南向           1.744e+05   3.78e+04      4.610      0.000         1e+05  2.49e+05\n",
      "西南向         -1.524e+06   2.08e+05     -7.326      0.000     -1.93e+06 -1.12e+06\n",
      "西向           8.511e+05   3.12e+05      2.731      0.006       2.4e+05  1.46e+06\n",
      "中           -2.754e+05   4.82e+04     -5.715      0.000      -3.7e+05 -1.81e+05\n",
      "高            5.478e+04   5.23e+04      1.047      0.295     -4.78e+04  1.57e+05\n",
      "==============================================================================\n",
      "Omnibus:                      140.017   Durbin-Watson:                   1.703\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              421.692\n",
      "Skew:                           0.197   Prob(JB):                     2.70e-92\n",
      "Kurtosis:                       4.863   Cond. No.                     1.53e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.53e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "X2 = sm.add_constant(X)\n",
    "est = sm.OLS(y, X2)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 透過AIC 選定最佳參數組合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictorcols = ['age', 'area', 'room', 'living_room', 'total_floor', '东南向', '东向', '南向', '西南向', '西向', '中', '高']\n",
    "import itertools\n",
    "AICs = {}\n",
    "for k in range(1,len(predictorcols)+1):\n",
    "    for variables in itertools.combinations(predictorcols, k):\n",
    "        predictors  = X[list(variables)]\n",
    "        predictors2 = sm.add_constant(predictors)\n",
    "        est = sm.OLS(y, predictors2)\n",
    "        res = est.fit()\n",
    "        AICs[variables] = res.aic    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  84670.325585499668),\n",
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       "  84673.843801994037),\n",
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       "  84678.757206266499),\n",
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       "  84681.895271412403),\n",
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       "  84684.520119580906),\n",
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       " (('living_room', 'total_floor', '东南向', '东向', '南向', '西向', '高'),\n",
       "  87960.628026948252),\n",
       " (('age', 'living_room', 'total_floor', '东南向', '南向', '高'), 87961.779124464694),\n",
       " (('age', 'living_room', 'total_floor', '西南向', '西向', '中'), 87963.119868423397),\n",
       " (('living_room', 'total_floor', '西南向', '高'), 87965.031834230962),\n",
       " (('living_room', 'total_floor', '东向', '高'), 87967.462214169165),\n",
       " (('living_room', 'total_floor', '南向', '西向', '中'), 87969.322033993958),\n",
       " (('living_room', 'total_floor', '西向', '高'), 87971.014047830176),\n",
       " (('age', 'living_room', 'total_floor', '东南向', '东向', '西南向'),\n",
       "  88013.453809905506),\n",
       " (('age', 'room', '东南向', '南向', '西向', '中', '高'), 88017.142649236965),\n",
       " (('living_room', 'total_floor', '东南向', '东向', '南向'), 88019.72285022719),\n",
       " (('living_room', 'total_floor', '东南向', '东向', '南向', '西向'), 88021.489764599697),\n",
       " (('age', 'living_room', '东南向', '南向', '中', '高'), 88022.442451685885),\n",
       " (('age', 'living_room', 'total_floor', '东南向', '南向'), 88023.527818924296),\n",
       " (('living_room', 'total_floor', '东向', '南向', '西南向', '西向'), 88024.416872738977),\n",
       " (('age', 'living_room', '东南向', '西南向', '西向', '中', '高'), 88026.151398126705),\n",
       " (('age', 'room', '东南向', '东向', '南向', '西南向', '高'), 88027.539611301516),\n",
       " (('age', 'living_room', 'total_floor', '东向'), 88029.466712523368),\n",
       " (('age', 'living_room', 'total_floor', '东向', '西向'), 88031.317641865884),\n",
       " (('living_room', 'total_floor', '西向'), 88032.534845012153),\n",
       " (('age', 'living_room', 'total_floor', '西向'), 88033.926569906413),\n",
       " (('age', 'living_room', '东南向', '西南向', '中'), 88036.386864094588),\n",
       " (('age', 'living_room', '东南向', '中'), 88037.591404254315),\n",
       " (('age', 'living_room', '东南向', '高'), 88038.567715610596),\n",
       " (('living_room', '东南向', '东向', '南向', '中'), 88039.356549852819),\n",
       " (('living_room', '东向', '西南向', '西向', '中', '高'), 88040.379176976057),\n",
       " (('living_room', '东南向', '东向', '南向', '西向', '中'), 88041.280855017802),\n",
       " (('age', 'living_room', '东向', '西南向', '中'), 88042.431899673131),\n",
       " (('age', 'living_room', '东向', '南向', '西南向', '中'), 88043.123288719667),\n",
       " (('age', 'living_room', '东向', '中'), 88043.81813246102),\n",
       " (('age', 'room', '东南向', '东向', '南向', '西向', '中'), 88044.313358353669),\n",
       " (('age', 'room', '南向', '高'), 88045.084827544139),\n",
       " (('age', 'room', '南向', '西南向', '高'), 88046.860506792727),\n",
       " (('living_room', '东向', '西南向', '中'), 88048.837036478886),\n",
       " (('age', 'living_room', '东向', '南向', '高'), 88050.465339777802),\n",
       " (('living_room', '东向', '南向', '中'), 88051.748781575225),\n",
       " (('age', 'living_room', '南向', '西向', '高'), 88053.13580276203),\n",
       " (('living_room', '高'), 88055.077215717363),\n",
       " (('living_room', '南向', '中'), 88056.394061724175),\n",
       " (('age', 'room', '西向', '中', '高'), 88060.223208078314),\n",
       " (('age', 'room', '南向', '西南向', '西向', '中'), 88065.071862554891),\n",
       " (('age', 'room', '西南向', '西向', '高'), 88067.06506010225),\n",
       " (('age', 'room', '东南向', '西南向', '中'), 88069.98494725296),\n",
       " (('age', 'living_room', '东南向', '东向', '南向', '西南向'), 88110.809290876583),\n",
       " (('age', 'living_room', '东南向', '西南向'), 88113.662749748648),\n",
       " (('living_room', '东南向', '东向'), 88114.814783540554),\n",
       " (('living_room', '东南向', '南向'), 88116.309092563941),\n",
       " (('age', 'living_room', '东向', '西南向'), 88124.190806024664),\n",
       " (('age', 'living_room', '东向', '南向', '西向'), 88127.09449176956),\n",
       " (('age', 'living_room', '南向', '西向'), 88128.789449174656),\n",
       " (('living_room', '西向'), 88131.665394622571),\n",
       " (('age', 'room', '东南向', '西南向', '西向'), 88137.912893455679),\n",
       " (('age', 'room', '东向', '西南向', '西向'), 88155.60982889001),\n",
       " (('room', '东南向', '南向', '中', '高'), 88289.047082388337),\n",
       " (('room', '东南向', '东向', '南向', '西南向', '高'), 88290.269413998642),\n",
       " (('room', '东南向', '东向', '南向', '西南向', '中', '高'), 88291.258449321569),\n",
       " (('room', '南向', '西南向', '高'), 88304.458625076106),\n",
       " (('room', '中', '高'), 88305.574735020258),\n",
       " (('room', '东向', '南向', '中', '高'), 88306.789971966457),\n",
       " (('room', '东南向', '南向', '西南向', '西向', '中'), 88326.12665805385),\n",
       " (('room', '东南向', '东向', '南向', '西向', '中'), 88327.932538077686),\n",
       " (('room', '南向', '西南向', '西向', '中'), 88341.23848353044),\n",
       " (('room', '西向', '中'), 88343.092036636604),\n",
       " (('room', '东南向', '东向', '西南向'), 88373.087304945075),\n",
       " (('room', '南向', '西南向', '西向'), 88386.838828560314),\n",
       " (('room', '东向', '南向'), 88389.230389477772),\n",
       " (('age', 'total_floor', '东南向', '东向', '南向', '西南向', '高'), 89152.908028481994),\n",
       " (('age', 'total_floor', '东南向', '东向', '西南向', '中', '高'), 89161.042640940461),\n",
       " (('age', 'total_floor', '南向', '西南向', '西向', '中', '高'), 89166.099460710175),\n",
       " (('age', 'total_floor', '东南向', '东向', '中', '高'), 89171.040968082176),\n",
       " (('age', 'total_floor', '东南向', '西南向', '西向', '高'), 89174.340847850253),\n",
       " (('age', 'total_floor', '南向', '西向', '中', '高'), 89178.703869702658),\n",
       " (('age', 'total_floor', '南向', '西向', '高'), 89182.218203420896),\n",
       " (('total_floor', '东南向', '东向', '南向', '西南向', '中', '高'), 89185.28210930941),\n",
       " (('total_floor', '东向', '南向', '西南向', '中', '高'), 89189.281755989257),\n",
       " (('age', 'total_floor', '东南向', '南向', '中'), 89194.494696727721),\n",
       " (('age', 'total_floor', '南向', '中'), 89200.799959997719),\n",
       " (('age', 'total_floor', '西向', '中'), 89205.540912272845),\n",
       " (('total_floor', '东向', '南向', '高'), 89210.742823238907),\n",
       " (('total_floor', '东南向', '东向', '西南向', '高'), 89217.51186084692),\n",
       " (('total_floor', '东向', '西南向', '中', '高'), 89221.099148586363),\n",
       " (('total_floor', '东南向', '东向', '西向', '中', '高'), 89224.216353071039),\n",
       " (('total_floor', '东南向', '西南向', '高'), 89227.595490703607),\n",
       " (('age', 'total_floor', '南向', '西南向'), 89231.840083284638),\n",
       " (('total_floor', '东南向', '西向', '高'), 89235.613801862652),\n",
       " (('total_floor', '东南向', '中', '高'), 89240.056399637135),\n",
       " (('total_floor', '东向', '南向', '西南向'), 89243.504805684381),\n",
       " (('total_floor', '高'), 89246.838861137614),\n",
       " (('total_floor', '东南向', '东向', '南向', '西向'), 89257.633363882444),\n",
       " (('total_floor', '中'), 89267.469993102713),\n",
       " (('total_floor', '南向'), 89281.989775527894),\n",
       " (('age', '东南向', '东向', '南向', '西向', '中', '高'), 89550.073934350163),\n",
       " (('age', '东南向', '东向', '西南向', '中', '高'), 89563.924592119001),\n",
       " (('age', '东南向', '东向', '高'), 89571.401963286233),\n",
       " (('age', '东南向', '南向', '西南向', '西向', '高'), 89576.560256548619),\n",
       " (('age', '东向', '南向', '高'), 89580.375979794597),\n",
       " (('age', '南向', '西向', '中', '高'), 89585.823607034254),\n",
       " (('age', '南向', '西南向', '中', '高'), 89591.277898002998),\n",
       " (('age', '南向', '西南向', '高'), 89597.658766823719),\n",
       " (('age', '东南向', '西南向', '西向', '中'), 89603.896760308387),\n",
       " (('age', '南向', '西南向', '中'), 89620.197361725615),\n",
       " (('age', '东南向', '东向', '西向'), 89664.049179809052),\n",
       " (('age', '东南向', '西向'), 89679.411846601026),\n",
       " (('age', '南向', '西向'), 89692.337389429362),\n",
       " (('东南向', '东向', '南向', '西南向', '西向', '高'), 89787.483576719707),\n",
       " (('东向', '南向', '西向', '高'), 89800.628721239991),\n",
       " (('东南向', '南向', '西南向', '中', '高'), 89805.766241823236),\n",
       " (('南向', '中', '高'), 89818.998754272208),\n",
       " (('东南向', '东向', '西南向', '中', '高'), 89827.199550296922),\n",
       " (('东南向', '高'), 89836.335733286192),\n",
       " (('东南向', '南向', '西南向', '西向', '中'), 89843.260524030935),\n",
       " (('高',), 89852.98070495468),\n",
       " (('东南向', '东向', '西南向', '中'), 89863.593300419961),\n",
       " (('东南向', '东向', '南向', '西南向'), 89880.777877180954),\n",
       " (('西南向', '中'), 89896.650877536551),\n",
       " (('东南向', '东向', '西南向', '西向'), 89916.316895255353),\n",
       " (('东向', '西南向'), 89936.865957051181)]"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "c = Counter(AICs)\n",
    "c.most_common()[::-10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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   "name": "python3"
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